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								Grad. Inst. of Networking &amp; Multimedia, Nat. Taiwan Univ., Taipei, Taiwan 
								
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								Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada 
								
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								Sch. of Software &amp; Microelectron., Peking Univ., Beijing, China 
								
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								Beijing Key Lab. of Intell. Telecommun. Software &amp; Multimedia, Beijing Univ. of Posts &amp; Telecommun., Beijing, China 
								
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								Dept. of Comput. Sci., Univ. of California, Davis, CA, USA 
								
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								Dept. of Inf. Manage., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan 
								
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								Coll. of Inf. Sci. &amp; Technol., Nanjing Univ. of Aeronaut. &amp; Astronaut., Nanjing, China 
								
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								Sch. of Comput. Sci. &amp; Inf., Univ. Coll. Dublin, Dublin, Ireland 
								
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								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/MCG.2009.78" target="blank">
									10.1109/MCG.2009.78
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										In this paper, the authors retrofitted a version of NodeTrix, a single-user graph visualization environment based on the InfoVis toolkit, to support multiple independent mice. The resulting low-cost environment was called CoCoNutTrix (Colocated Collaborative NodeTrix). Then, it was assessed how analysts viewed CoCoNutTrix <span class="snippet">and</span> whether it effectively supported collaborative data analysis among domain experts using real data sets for social-network analysis. Our goal is to refine <span class="snippet">and</span> expand our knowledge about retrofitting <span class="snippet">and</span> hence designing colocated collaborative- visualization systems.
										
						                	
						                	
						                	
						                			
						             				
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										Multi-relational Network Analysis for Covert Organizations
									</a>
								
							
						
					
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						Sun Duo-yong; Guo Shu-Quan; Liu Xiao-Peng; Li Jiang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6059524">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics Conference (EISIC), 2011 European
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/EISIC.2011.28" target="blank">
									10.1109/EISIC.2011.28
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Rapid development of social network theory provides new perspective for the study of covert organizational behavior <span class="snippet">and</span> becomes new research hot topic in the international academic circle. Conventional covert organization network research mainly involves interpersonal relationships among members <span class="snippet">and</span> there are some limitations. Problems that need to be solved at present include introducing more elements according to the nature of covert organization activities, more appropriately describing characteristics of organization activities <span class="snippet">and</span> improving pertinence of disintegration strategies. This paper presents construction <span class="snippet">and</span> analysis methods of multi-relational network of covert organizations from multiple perspectives of interpersonal relationships, resources, skills <span class="snippet">and</span> tasks, conducts empirical study combining typical organizational activities <span class="snippet">and</span> provides new approach for deeper understanding of characteristics of covert organization activities <span class="snippet">and</span> construction of disintegration strategies.
										
						                	
						                	
						                	
						                			
						             				
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										Tweets mining for French Presidential Election
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						Wegrzyn-Wolska, K.; Bougueroua, L. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6396507">
			                	<br>Computational Aspects of Social Networks (CASoN), 2012 Fourth International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								<a href="http://dx.doi.org/10.1109/CASoN.2012.6412392" target="blank">
									10.1109/CASoN.2012.6412392
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										This paper describes a system which surveys the French Presidential Election trends from Twitter's discussions. This system carries out the automatic collection, evaluation <span class="snippet">and</span> rating of tweets for evaluating the trends. The objective of this paper is to discuss the variety of issues <span class="snippet">and</span> challenges surrounding the perspectives regarding the use of Social Network Analyses <span class="snippet">and</span> Text Mining methods for applications in politics. The article will first present a review of the literature in Social Media <span class="snippet">and</span> Text Mining analysis for political purposes <span class="snippet">and</span> then describe the work done for our system for collecting <span class="snippet">and</span> manipulating Twitter's data.
										
						                	
						                	
						                	
						                			
						             				
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										Towards Efficient Reality Mining with Contexts <span class="snippet">and</span> Semantics: A Case Study of Telecommunication
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						Jung, J.J.; Hojin Lee; Kwang-Sun Choi 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4739514">
			                	<br>Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
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		       			<br>  
						Volume:	3					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/IITA.2008.115" target="blank">
									10.1109/IITA.2008.115
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2008
							
										
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										Recommendation systems need to employ efficient reality mining module to find out whether social relationships extracted from a collected dataset are really meaningful in real world. Also, the recommendation should be generated by taking current contexts (e.g., location <span class="snippet">and</span> time) account into. In this paper, we want to propose an ontology-based framework to discover real social networks from a large amount of dataset. This framework can provide a semiautomatic approach to build meaningful social networks by repeating interactions with human experts. We assume that given a dataset, we have to discover the hidden social networks which express the contextual dependencies between people. The personal context of a certain person is interrelated with those of other people, <span class="snippet">and</span> investigated how to take into account his neighborpsilas contexts, which possibly have an important influence on his personal context. Particularly, in this research project, the target dataset in this work is collected from user activities (about over two millions of people) in telecommunication industry. We have applied the proposed system to discover the social networks among the mobile subscribers to provide recommendation to mobile devices.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231898&contentType=Conference+Publications&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Social Network Model Based on Keyword Categorization
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						Bhattacharyya, P.; Garg, A.; Wu, S.F. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.46" target="blank">
									10.1109/ASONAM.2009.46
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										A user profile on an <span class="snippet">online</span> social network is characterized by its profile entries (keywords). In this paper, we study the relationship between semantic similarity of user keywords <span class="snippet">and</span> the social network topology. First, we present a 'forest' model to categorize keywords <span class="snippet">and</span> define the notion of distance between keywords across multiple categorization trees (i.e., a forest). Second, we use the keyword distance to define similarity functions between a pair of users <span class="snippet">and</span> show how social network topology can be modeled accordingly. Third, we validate our social network topology model, using a simulated social graph, against a real life social graph dataset.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4644061&contentType=Journals+%26+Magazines&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										<span class="snippet">Social network analysis</span> <span class="snippet">and</span> visualization [Applications Corner]
									</a>
								
							
						
					
				</h3>
				
					
						Brandes, U. 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=79">
			               		<br>Signal Processing Magazine, IEEE
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	25					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4644043">
							Issue: 6	
						</a>						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967042">
								Power, Energy, &amp; Industry Applications</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/MSP.2008.929814" target="blank">
									10.1109/MSP.2008.929814
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2008
							
										
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							147	- 151
													
													
						
						
						 
						
					
					
					
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									 Cited by 5
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										The notion of a social network, <span class="snippet">and</span> associated methods of analysis, is permeating an increasing number of application domains, including, e.g., communications, bibliometry, <span class="snippet">and</span> systems biology. This article provides a short introduction, <span class="snippet">and</span> an overview of the foundations of this field, focussing primarily, but not excusively, on methods.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6111127&contentType=Conference+Publications&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A hybrid approach of trust inference <span class="snippet">and</span> <span class="snippet">social network analysis</span> for expert search
									</a>
								
							
						
					
				</h3>
				
					
						Wei-Feng Tung; Shih-Hao Tseng 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6095975">
			                	<br>Nano, Information Technology <span class="snippet">and</span> Reliability (NASNIT), 2011 15th North-East Asia Symposium on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/NASNIT.2011.6111127" target="blank">
									10.1109/NASNIT.2011.6111127
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										This study combines a trust inference (The `Tidal Trust' algorithm) <span class="snippet">and</span> <span class="snippet">social network analysis</span> (SNA) to provide a new way of searching the experts (e.g., physicians) from the blog's articles. The hybrid approach to measure the trust values <span class="snippet">and</span> centralities of social networking through the interactions of blogs' trackbacks can facilitate searching the trustable blog's articles <span class="snippet">and</span> expert recommendations for the query users/bloggers. Furthermore, knowledge dissemination <span class="snippet">and</span> acquirement can also be improved by the bloggers' interactions <span class="snippet">and</span> the appraisals assigned by the bloggers on the blog's web sites. In order to verify the effect of hybrid approach, the experiments test the variation of recommendation values inferred by trust value <span class="snippet">and</span> rating based on 50 blogs' articles <span class="snippet">and</span> 20 blogs' authors. The results demonstrate the high trust value the high recommendation value. In terms of the variation of recommendation values, the high trust value is lower than the low trust value. The quality of recommendation results would be stable. The experimental results demonstrate the rating is also important to influence the recommendation value in this trust-based recommendation methodology.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5729637&contentType=Conference+Publications&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Influencing Massive Multi-agent Systems via Viral Trait Spreading
									</a>
								
							
						
					
				</h3>
				
					
						McLaughlan, B.; Hexmoor, H. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5729428">
			                	<br>Self-Adaptive <span class="snippet">and</span> Self-Organizing Systems Workshop (SASOW), 2010 Fourth IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SASOW.2010.59" target="blank">
									10.1109/SASOW.2010.59
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							287	- 292
													
													
						
						
						 
						
					
					
					
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									<p>
										This paper describes a method by which a massive multi-agent system can be influenced without resorting to micromanagement. This method could be utilized in the development of meta-reasoning components of individual agents. Agents in the system adopt the traits of their successful peers. The administrator guides this spread of traits through selectively injecting influential agents with modified traits. These key agents are identified via <span class="snippet">social network analysis</span> techniques. Experimentation is described in which the system is tested for its ability to automatically adopt an acceptable configuration as well as testing the ease in which the administrator is able to guide the system to a better configuration.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5283479&contentType=Conference+Publications&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Mining <span class="snippet">and</span> Analyzing Multirelational Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Stroele, V.; Oliveira, J.; Zimbrao, G.; Souza, J.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5282954">
			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	4					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CSE.2009.69" target="blank">
									10.1109/CSE.2009.69
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										A social network is a social structure consisting of individuals or organizations, usually represented by nodes tied by one or more types of interdependency or relationship. This work focuses on using data mining techniques to identify intra <span class="snippet">and</span> inter organization groups of people with similar profiles that could have relationships amongst them. For this, we construct a multi relational scientific social network where researchers may have four different types of relations with each other. In this paper, we analyze the scientific scenario in computing science in Brazil, assessing how researchers in the best universities <span class="snippet">and</span> research centres collaborate <span class="snippet">and</span> relate to each other.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5563066&contentType=Conference+Publications&pageNumber%3D7%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Case Study of Open Source <span class="snippet">and</span> Public Participation in Catalyzing Social Innovations
									</a>
								
							
						
					
				</h3>
				
					
						Liu, H.K.; Sandfort, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.76" target="blank">
									10.1109/ASONAM.2010.76
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										Our study investigates the use of a new open source platform in catalyzing social innovations <span class="snippet">and</span> participation of its members over time. We empirically examined how the nature of project designs <span class="snippet">and</span> social pressure affect contribution to the open source platform. In the twenty-one projects (3, 998 contributions) from 2004 to 2009, we find that the average number of contributions is higher when the projects are highly visible, when the project is designed to require specific skills from participants, <span class="snippet">and</span> when it requires outcome measurement from participants' proposals. Also, we verified that actors join collective action when they believe their contribution is meaningful <span class="snippet">and</span> they would stop when they believe their contribution could be marginal. These results provide implications for open source platform design in the philanthropic sector.
										
						                	
						                	
						                	
						                			
						             				
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										A Comparison of Objective Functions in Network Community Detection
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						Chuan Shi; Yanan Cai; Yu, P.S.; Zhenyu Yan; Bin Wu 
					
					
						
						
							            
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										Community detection, as an important unsupervised learning problem in <span class="snippet">social network analysis</span>, has attracted great interests in various research areas. Many objective functions for community detection that can capture the intuition of communities have been introduced from different research fields. Based on the classical single objective optimization framework, this paper compares a variety of these objective functions <span class="snippet">and</span> explores the characteristics of communities they can identify. Experiments show most objective functions have the resolution limit <span class="snippet">and</span> their communities structure have many different characteristics.
										
						                	
						                	
						                	
						                			
						             				
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										Community Evolution in Dynamic Social Networks -- Challenges <span class="snippet">and</span> Problems
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						Takaffoli, M. 
					
					
						
						
							            
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										Information networks that describe the relationship between individuals are called social networks <span class="snippet">and</span> are usually modeled by a graph structure. <span class="snippet">Social network analysis</span> is the study of these information networks which leads to uncover patterns of interaction among the entities. Most social networks are dynamic, <span class="snippet">and</span> studying the evolution of these networks over time could provide insight into the changes that occurred in the iteration patterns <span class="snippet">and</span> also the future trends of the networks. Furthermore, in a dynamic scenario, communities, which are groups of densely interconnected nodes, are affected by changes in the underlying population. The analysis of communities <span class="snippet">and</span> their evolutions can help determine the characteristics <span class="snippet">and</span> structural properties of the network. Here, we provide a brief overview of the existing research in the area of dynamic <span class="snippet">social network analysis</span>, their limitations, <span class="snippet">and</span> the challenges that are exists for further analysis.
										
						                	
						                	
						                	
						                			
						             				
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